Popular Software Architecture Patterns Developers Must Know in 2024

Software architecture provides the high-level structure of a software system. It establishes the system’s overall components, interactions and boundaries to meet critical requirements like scalability, reliability, and performance.

Software architecture patterns are reusable templates that provide standardized ways to organize systems and promote desired quality attributes. They capture proven experience in architecting software systems and provide best practices to structure applications.

Some examples of common architectural patterns include layered architecture, microservices, event-driven architecture, and model-view-controller. Like design patterns provide idioms to solve programming problems, architecture patterns aim to solve architectural challenges in building software systems.

Quality and Efficiency Maintenance

Using proven architecture patterns helps develop high-quality software while improving efficiency. The reusable models optimize workflows, reduce bugs, and solve common development issues.

Agility
Choosing an appropriate software architecture provides flexibility to easily handle future enhancements and modifications in software development’s early and late stages.

Problem-Solving
Boosting standard architectural principles clarifies the project scope and helps teams grasp the status rapidly. This improves coordination and accelerates productivity.

Choosing the right patterns and practices can make development much smoother. Here are 12 software architecture patterns that every developer should know about:

Layered Architecture
This divides the application into layers like presentation, business, and data access. Each layer has distinct responsibilities and lower layers are reusable across applications. This separation of concerns enables easier maintenance and parallel development.
For example, the presentation layer handles UI, the business layer manages logic, and the data layer interacts with the database. Since layers have well-defined interfaces, changes in one layer don’t impact others much.

• Isolates UI, business logic and data access concerns
• Abstracts details of lower layers from higher ones
• Lower layers can be reused across applications
• Enables parallel development and testing of layers
• Changes limited to within layer without affecting others
• Changes limited to within layer without affecting others
• Improves maintainability and modifiability

Layered architecture also has some disadvantages:

• Too many layers can increase complexity
• Lots of chatty inter-layer calls can affect performance
• Strong separation of layers reduces cohesion

So the number and boundaries of layers must be carefully designed. But overall, layered architecture helps organize applications and enables scaling them.

Model-View-Controller

MVC separates UI (view), business logic (controller), and data (model). It improves separation of concerns. The view handles UI, the model manages data, and the controller responds to user input and updates the model and view accordingly.
For instance, a button click by the user triggers the controller. It then reads/writes data from/to the model and refreshes the view to display updated data. This isolation of responsibilities simplifies code maintenance.

Benefits of MVC:

• Clean separation of UI, logic and data models
• Multiple views can work with same model
• Business logic centralized in the controller
• Parallel development possible
• Improves testability and reusability
• Changes isolated to within MVC component

Like any pattern, MVC also has some limitations:

• Complex apps can have a lot of bidirectional dependencies between MVC components
• There is no consensus on where some code like data validation should reside
• Views and controller can sometimes become tightly coupled

Overall, MVC enables building well-structured applications by separating key concerns into modular components. It is one of the most widely used architecture patterns.

Microservices

This breaks the app into independently deployable services by functionality. Services can be implemented using different languages and technologies based on the need. It enables agile development and easy scalability.
For example, the ordering and payment services can be separate microservices. This also isolates failures. If the ordering service goes down, payments can still function. Microservices are great for large, complex applications.

Microservices provide the following advantages:

• Modular services can be developed independently by teams
• Individual services can be scaled up or down
• Technology heterogeneity – use what suits each service
• Fault isolation – one service failure doesn’t cascade
• Improves maintainability
• Enables continuous delivery and deployment

Microservices also come with a few challenges:

• Increased complexity to coordinate services
• Repeated functionality across services
• Data consistency challenges across services
• Network latency between inter-service calls
• Debugging and monitoring inter-service apps

So microservices architecture works very well for large apps where decentralized teams and frequent changes are required.

Event-driven

Components communicate by generating and consuming events. This helps in decoupling components in time and space. Services can emit events without knowing what will handle them. Loosely coupled components can be more easily changed.

For instance, on a new order, the ordering service emits an event. The payment and notification services listen and act on it. Services are isolated by only sharing agreed events.

Benefits of event-driven architecture:

• Loosely coupled components
• Separation of event producers and consumers
• Async event processing increases scalability
• Easy to add new consumers by subscribing to events
• Enables integration across apps through shared events

Event-driven architecture also comes with some challenges:

• Debugging and monitoring can be harder
• Duplicate events or replay of old events must be handled
• Event schema evolution needs to be managed carefully
• Testing requires simulating events and outputs

By using events for integration and asynchronous flows, event-driven architecture promotes building decoupled and scalable applications.

Pipe and Filter

This breaks down processing into separate steps like an assembly line. Output of one step is input for the next. This enables parallel execution and reordering of steps.

For example, an order processing pipeline could validate order, calculate discounts, apply taxes, and persist to database in separate filter components. Filters have single responsibilities making them reusable.

Some key aspects of pipe and filter architecture:

• Each filter has a single capability
• Filters are independent and reusable
• Execution order of filters can be changed
• Easily add or remove filters
• Parallel execution of filters possible for performance
• Good for complex processing workflows

Pipe and filter also comes with some disadvantages:

• Filter dependencies must be carefully managed
• Debugging complex pipelines can be challenging
• Filters sharing state can cause issues
• Lots of pipes and filters can impact performance

Overall, it provides a great way to break down complex processing chains into modular, flexible steps.

Client-Server

Logic is split between client and server. Clients only handle the UI. Servers centrally manage the bulk of business logic and data storage. This separation of concerns enables reuse and sharing of capabilities.

For instance, multiple client apps can talk to a shared backend server. Business logic resides on the server so clients remain thin. Servers can be scaled independently of clients.

Some benefits of client-server architecture:

• Separation of client UI concerns and server logic/data concerns
• Server logic centralized and reused across clients
• Server can be scaled as per load, clients can be simple
• Client and server can be on different technology stacks
• Statelessness allows for client failover

Certain limitations of client-server architecture:

• Chatty client-server communication can affect performance
• Not optimal for very thick/heavy clients
• If server goes down, clients are cut-off
• Hardware upgrades needed on server side can disrupt clients
• Network latency can impact client performance

Still, the separation between client and server enabled by this pattern is very useful for building networked applications.

Service-Oriented Architecture

Key application capabilities are provided as discoverable services with well-defined interfaces. Services can be called over a network to support interoperability between systems.

For example, a weather service provides real-time weather data. Many different apps can consume this service. Services are self-contained and loosely coupled promoting reuse.

Some principles of service-oriented architecture:

• Services are self-contained and platform independent
• Services can be discovered and invoked across the network
• Services have well-defined interfaces that hide implementation
• Services are loosely coupled and communicate via message passing
• Services can be orchestrated to build workflows

Service-oriented architecture also comes with some challenges:

• Increased complexity to create many different services
• Chatty service interactions can impact performance
• Debugging issues across service calls can be difficult
• Versioning services while maintaining backward compatibility
• Managing shared data consistently across services

Overall, SOA provides a flexible way to build integrated ecosystems using standards-based, reusable services.

Messaging Architecture

Components interact by sending messages to each other instead of direct API calls. This reduces coupling as sender and receiver don’t need to know each other. Messages can be reliably queued and processed asynchronously.

For instance, after creating an order, the ordering service sends a message to the shipping service to dispatch the order. The services are isolated and can scale independently.

Some benefits of messaging architecture:

• Loosely coupled components
• Async message processing and queuing improves scalability
• Fault isolation and reliability using retries and dead letter queues
• Easier integration across different platforms
• Messages enable auditability and observability

Messaging also comes with some challenges:

• Added complexity of managing queues and messages
• Debugging and monitoring message flows is difficult
• No baked-in request/response. Needs additional plumbing
• Duplicate messages and replay need to be handled
• Ordering and transactions challenges with async messaging

So messaging systems provide reliable, asynchronous integration between decoupled components.

Blackboard
Components communicate by producing data to a centralized “blackboard” which stores it for others to read. This decouples data producers from consumers. Useful for AI/ML systems.
For example, different sensors publish sensor data to a blackboard. The AI system reads the data and makes decisions which are also published on the blackboard for actuators to act on.

Some key aspects of blackboard architecture:

• Allows components to produce and consume data independently
• Components are isolated – only communicate via blackboard
• Good for data-intensive systems like AI/ML
• Removes tight coupling between producers and consumers
• Additional components can be added by integrating with blackboard

Some limitations of blackboard architecture:

• Changes need to be carefully controlled to not disrupt blackboard
• Scale and performance depends heavily on blackboard implementation
• Debugging and observability can be challenging
• Blackboard can become very complex with many systems integrating
• Data schema agreement required across components

Overall, blackboard pattern works well for systems like AI/ML where independent components share data through a common medium.

Representational State Transfer (REST)

RESTful services expose API endpoints that clients can access to manage resources using standard HTTP methods like GET, POST, PUT, DELETE.
For instance, GET /users returns a list of users, POST /users creates a user, etc. REST APIs provide a uniform interface for accessing services in a simplified way.

Some of the key benefits of REST APIs:

• Lightweight, simple and flexible
• Scalable since REST is stateless
• Cacheable to improve performance
• Leverages existing HTTP infrastructure
• Wide ecosystem of tools available
• Better separation of concerns compared to SOAP

REST also comes with some limitations:

• Not standardized so APIs can vary across implementations
• Not optimized for complex operations and data queries
• Statelessness makes managing user sessions/state trickier
• More verbose payloads due to less optimizations
• Discoverability not directly supported – depends on documentation

Overall, REST APIs are simple, easy to implement and scale well. This has led to their widespread adoption across the industry.

Space-Based Architecture

Components interact by publishing data and subscribing to data feeds. Components are decoupled in time and space. Great for real-time apps and enabling reactive capabilities.
For example, the GPS publishes location events. The navigation app subscribes to location updates and plots the moving position on its map. Space-based architectures promote loose coupling.

Some key principles of space-based architecture:

• Components produce and consume data independently
• Components communicate asynchronously via data streams
• Reactive systems can detect changes and react instantly
• Easy to add new capabilities by adding streams and subscribers
• Reduces coupling and improves scalability and throughpu

Space-based architecture also comes with some challenges:

• Debugging and testing is harder with async, non-deterministic flows
• Duplicate events/replay need to be handled properly
• Components agree on stream schemas and semantics
• State management can be trickier across components
• Network latency impacts real-time notification performance

So space-based architecture works great for real-time systems like IoT that require reactive capabilities, loose coupling and high throughput.

Implicit Invocation

Components can trigger actions in other components without directly calling them. The framework handles passing data and invoking dependent actions implicitly.
For instance, in Excel, updating a cell implicitly recalculates formulas in other cells dependent on it. The cells don’t directly interact. This approach removes dependencies between components.

Some key benefits of implicit invocation:

• Removes tight coupling between components
• Simplifies wiring dependencies; handled by framework
• Enables building reactive systems
• Components can be added/changed more easily
• Changes limited to within component as dependencies abstracted

Some drawbacks of implicit invocation:

• Difficult to debug and test complex component wiring
• Added cognitive load to understand component relationships
• External dependency management relies on conventions
• Changes can unintentionally impact other components
• Framework dictates how components interact and scale

So implicit invocation works best for frameworks and platforms where the framework handles component linking and dependency management.

TAV Tech Solutions is a leading software development company specializing in offering a complete range of software service and technology solutions across industry verticals. Creating software from scratch without proper knowledge of architecture patterns can become an absolute disaster. Experts at TAV Tech Solutions make use of standardized practices to deliver stable software solutions for businesses.